Article
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PAA: Persian Author Attribution Using Dense and Recursive Connection
Version 1
: Received: 15 May 2024 / Approved: 20 May 2024 / Online: 20 May 2024 (12:07:49 CEST)
How to cite: Najafi, M.; Sadidpur, S. PAA: Persian Author Attribution Using Dense and Recursive Connection. Preprints 2024, 2024051258. https://doi.org/10.20944/preprints202405.1258.v1 Najafi, M.; Sadidpur, S. PAA: Persian Author Attribution Using Dense and Recursive Connection. Preprints 2024, 2024051258. https://doi.org/10.20944/preprints202405.1258.v1
Abstract
Abstract: Author attribution refers to identifying the author of a document by distinguishing between texts written by various authors. Since the author’s writing style is a straightforward concept of his writing habits, an author’s attribution can be used in various sections of society, including legal work, court cases, and plagiarism. This paper studies and compares a deep architecture that automatically identifies Persian writers through their writing styles. For this purpose, since there were no existing datasets suitable for author identification in Persian, the first step was to collect three different datasets: Novels, News, and Twitter. Afterward, the Word2Vec algorithm is trained on many Persian datasets and used as word embedding. To have syntactic features besides semantic ones, Part Of Speech (POS) embedding has also been used. In the next step, an attention mechanism and Long Short-Term Memory (LSTM) are coupled with residual connections. Thus, the current layer input becomes the output of all previous layers rather than the top one. The model implemented is superior in performance for all three types of data compared to earlier models in text classification and author attribution. Absolute accuracies of 90.22%, 85.10%, and 71.39% were achieved among the three Novels, News, and Tweets datasets.
Keywords
author attribution;Word2Vec; skip connections; attention mechanism
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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